130 research outputs found
Online learning in repeated auctions
Motivated by online advertising auctions, we consider repeated Vickrey
auctions where goods of unknown value are sold sequentially and bidders only
learn (potentially noisy) information about a good's value once it is
purchased. We adopt an online learning approach with bandit feedback to model
this problem and derive bidding strategies for two models: stochastic and
adversarial. In the stochastic model, the observed values of the goods are
random variables centered around the true value of the good. In this case,
logarithmic regret is achievable when competing against well behaved
adversaries. In the adversarial model, the goods need not be identical and we
simply compare our performance against that of the best fixed bid in hindsight.
We show that sublinear regret is also achievable in this case and prove
matching minimax lower bounds. To our knowledge, this is the first complete set
of strategies for bidders participating in auctions of this type
Computing CQ lower-bounds over OWL 2 through approximation to RSA
Conjunctive query (CQ) answering over knowledge bases is an important
reasoning task. However, with expressive ontology languages such as OWL, query
answering is computationally very expensive. The PAGOdA system addresses this
issue by using a tractable reasoner to compute lower and upper-bound
approximations, falling back to a fully-fledged OWL reasoner only when these
bounds don't coincide. The effectiveness of this approach critically depends on
the quality of the approximations, and in this paper we explore a technique for
computing closer approximations via RSA, an ontology language that subsumes all
the OWL 2 profiles while still maintaining tractability. We present a novel
approximation of OWL 2 ontologies into RSA, and an algorithm to compute a
closer (than PAGOdA) lower bound approximation using the RSA combined approach.
We have implemented these algorithms in a prototypical CQ answering system, and
we present a preliminary evaluation of our system that shows significant
performance improvements w.r.t. PAGOdA.Comment: 26 pages, 1 figur
Profiling and Evolution of Intellectual Property
In recent years, with the rapid growth of Internet data, the number and types
of scientific and technological resources are also rapidly expanding. However,
the increase in the number and category of information data will also increase
the cost of information acquisition. For technology-based enterprises or users,
in addition to general papers, patents, etc., policies related to technology or
the development of their industries should also belong to a type of scientific
and technological resources. The cost and difficulty of acquiring users.
Extracting valuable science and technology policy resources from a huge amount
of data with mixed contents and providing accurate and fast retrieval will help
to break down information barriers and reduce the cost of information
acquisition, which has profound social significance and social utility. This
article focuses on the difficulties and problems in the field of science and
technology policy, and introduces related technologies and developments.Comment: 11 pages. arXiv admin note: text overlap with arXiv:2203.1259
SDDs are Exponentially More Succinct than OBDDs
Introduced by Darwiche (2011), sentential decision diagrams (SDDs) are
essentially as tractable as ordered binary decision diagrams (OBDDs), but tend
to be more succinct in practice. This makes SDDs a prominent representation
language, with many applications in artificial intelligence and knowledge
compilation. We prove that SDDs are more succinct than OBDDs also in theory, by
constructing a family of boolean functions where each member has polynomial SDD
size but exponential OBDD size. This exponential separation improves a
quasipolynomial separation recently established by Razgon (2013), and settles
an open problem in knowledge compilation
- …